






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


A_						:						str						      =					logging.get_logger(__name__)

A_						:						Dict						      =					{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A_						:						Optional[int]						      =					{
    'vocab_file': {
        'google/realm-cc-news-pretrained-embedder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-encoder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-scorer': (
            'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-openqa': (
            'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'
        ),
        'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt',
        'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt',
        'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt',
        'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'google/realm-cc-news-pretrained-embedder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'
        ),
        'google/realm-cc-news-pretrained-encoder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'
        ),
        'google/realm-cc-news-pretrained-scorer': (
            'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'
        ),
        'google/realm-cc-news-pretrained-openqa': (
            'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'
        ),
        'google/realm-orqa-nq-openqa': (
            'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-nq-reader': (
            'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-wq-openqa': (
            'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-wq-reader': (
            'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'
        ),
    },
}

A_						:						Union[str, Any]						      =					{
    'google/realm-cc-news-pretrained-embedder': 512,
    'google/realm-cc-news-pretrained-encoder': 512,
    'google/realm-cc-news-pretrained-scorer': 512,
    'google/realm-cc-news-pretrained-openqa': 512,
    'google/realm-orqa-nq-openqa': 512,
    'google/realm-orqa-nq-reader': 512,
    'google/realm-orqa-wq-openqa': 512,
    'google/realm-orqa-wq-reader': 512,
}

A_						:						Tuple						      =					{
    'google/realm-cc-news-pretrained-embedder': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-encoder': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-scorer': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-openqa': {'do_lower_case': True},
    'google/realm-orqa-nq-openqa': {'do_lower_case': True},
    'google/realm-orqa-nq-reader': {'do_lower_case': True},
    'google/realm-orqa-wq-openqa': {'do_lower_case': True},
    'google/realm-orqa-wq-reader': {'do_lower_case': True},
}




class 							_lowerCAmelCase(			UpperCAmelCase_   ):





       """simple docstring"""







       a							:						str										=VOCAB_FILES_NAMES
       a							:						Any										=PRETRAINED_VOCAB_FILES_MAP
       a							:						List[Any]										=PRETRAINED_INIT_CONFIGURATION
       a							:						Optional[Any]										=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a							:						List[str]										=RealmTokenizer





       def __init__(   self    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     _lowerCamelCase=True    ,     _lowerCamelCase="[UNK]"    ,     _lowerCamelCase="[SEP]"    ,     _lowerCamelCase="[PAD]"    ,     _lowerCamelCase="[CLS]"    ,     _lowerCamelCase="[MASK]"    ,     _lowerCamelCase=True    ,     _lowerCamelCase=None    ,     **_lowerCamelCase    ,     ):
         super().__init__(
             _lowerCamelCase    ,     tokenizer_file=_lowerCamelCase    ,     do_lower_case=_lowerCamelCase    ,     unk_token=_lowerCamelCase    ,     sep_token=_lowerCamelCase    ,     pad_token=_lowerCamelCase    ,     cls_token=_lowerCamelCase    ,     mask_token=_lowerCamelCase    ,     tokenize_chinese_chars=_lowerCamelCase    ,     strip_accents=_lowerCamelCase    ,     **_lowerCamelCase    ,     )

         UpperCamelCase_:   List[Any]        =							json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
         if (
             normalizer_state.get('lowercase'    ,     _lowerCamelCase				) != do_lower_case
             or normalizer_state.get('strip_accents'    ,     _lowerCamelCase				) != strip_accents
             or normalizer_state.get('handle_chinese_chars'    ,     _lowerCamelCase				) != tokenize_chinese_chars
         ):
           UpperCamelCase_:   Tuple        =							getattr(_lowerCamelCase    ,     normalizer_state.pop('type'				)				)
           UpperCamelCase_:   Any        =							do_lower_case
           UpperCamelCase_:   Tuple        =							strip_accents
           UpperCamelCase_:   Optional[Any]        =							tokenize_chinese_chars
           UpperCamelCase_:   List[str]        =							normalizer_class(**_lowerCamelCase				)

         UpperCamelCase_:   Optional[Any]        =							do_lower_case





       def      _a       (   self    ,     _lowerCamelCase    ,     **_lowerCamelCase				):
         UpperCamelCase_:   Optional[Any]        =							PaddingStrategy.MAX_LENGTH

         UpperCamelCase_:   Any        =							text
         UpperCamelCase_:   List[Any]        =							kwargs.pop('text_pair'    ,     _lowerCamelCase				)
         UpperCamelCase_:   Optional[int]        =							kwargs.pop('return_tensors'    ,     _lowerCamelCase				)

         UpperCamelCase_:   Optional[int]        =							{
             'input_ids': [],
             'attention_mask': [],
             'token_type_ids': [],
         }

         for idx, candidate_text in enumerate(_lowerCamelCase				):
           if batch_text_pair is not None:
             UpperCamelCase_:   Union[str, Any]        =							batch_text_pair[idx]
           else:
             UpperCamelCase_:   Any        =							None

           UpperCamelCase_:   Any        =							super().__call__(_lowerCamelCase    ,     _lowerCamelCase    ,     return_tensors=_lowerCamelCase    ,     **_lowerCamelCase				)

           UpperCamelCase_:   Dict        =							encoded_candidates.get('input_ids'				)
           UpperCamelCase_:   str        =							encoded_candidates.get('attention_mask'				)
           UpperCamelCase_:   int        =							encoded_candidates.get('token_type_ids'				)

           if encoded_input_ids is not None:
             output_data["input_ids"].append(_lowerCamelCase				)
           if encoded_attention_mask is not None:
             output_data["attention_mask"].append(_lowerCamelCase				)
           if encoded_token_type_ids is not None:
             output_data["token_type_ids"].append(_lowerCamelCase				)

         UpperCamelCase_:   Dict        =							{key: item for key, item in output_data.items() if len(_lowerCamelCase				) != 0}

         return BatchEncoding(_lowerCamelCase    ,     tensor_type=_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase=None				):
         UpperCamelCase_:   Union[str, Any]        =							[self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
           output += token_ids_a + [self.sep_token_id]

         return output





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase = None				):
         UpperCamelCase_:   Union[str, Any]        =							[self.sep_token_id]
         UpperCamelCase_:   Dict        =							[self.cls_token_id]
         if token_ids_a is None:
           return len(cls + token_ids_a + sep				) * [0]
         return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase = None				):
         UpperCamelCase_:   Tuple        =							self._tokenizer.model.save(_lowerCamelCase    ,     name=_lowerCamelCase				)
         return tuple(_lowerCamelCase				)



def 							UpperCamelCase_(					_A :Union[str, Any]	)->		List[str]:
			UpperCamelCase__       =							[0] * len(_A	)
			UpperCamelCase__       =							[]
			UpperCamelCase__       =							[]
			UpperCamelCase__       =							0

			for values in graph.values():
						for i in values:
									indegree[i] += 1

			for i in range(len(_A	)	):
						if indegree[i] == 0:
									queue.append(_A	)

			while queue:
						UpperCamelCase__       =							queue.pop(0	)
						cnt += 1
						topo.append(_A	)
						for x in graph[vertex]:
									indegree[x] -= 1
									if indegree[x] == 0:
												queue.append(_A	)

			if cnt != len(_A	):
						print("Cycle exists"	)
			else:
						print(_A	)


# Adjacency List of Graph
__UpperCamelCase     		=							{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)


